1 citations · 1 across the 7 of their papers we have counts for
7 papers
Mel-Refine: A Plug-and-Play Approach to Refine Mel-Spectrogram in Audio Generation
Hongming Guo, Ruibo Fu, Yizhong Geng +9
Text-to-audio (TTA) model is capable of generating diverse audio from textual prompts. However, most mainstream TTA models, which predominantly rely on Mel-spectrograms, still face…
EELE: Exploring Efficient and Extensible LoRA Integration in Emotional Text-to-Speech
Xin Qi, Ruibo Fu, Zhengqi Wen +10
In the current era of Artificial Intelligence Generated Content (AIGC), a Low-Rank Adaptation (LoRA) method has emerged. It uses a plugin-based approach to learn new knowledge with…
ICAGC 2024: Inspirational and Convincing Audio Generation Challenge 2024
Ruibo Fu, Rui Liu, Chunyu Qiang +11
The Inspirational and Convincing Audio Generation Challenge 2024 (ICAGC 2024) is part of the ISCSLP 2024 Competitions and Challenges track. While current text-to-speech (TTS) techn…
A multi-speaker multi-lingual voice cloning system based on vits2 for limmits 2024 challenge
Xiaopeng Wang, Yi Lu, Xin Qi +4
This paper presents the development of a speech synthesis system for the LIMMITS'24 Challenge, focusing primarily on Track 2. The objective of the challenge is to establish a multi…
Codecfake: An Initial Dataset for Detecting LLM-based Deepfake Audio
Yi Lu, Yuankun Xie, Ruibo Fu +9
With the proliferation of Large Language Model (LLM) based deepfake audio, there is an urgent need for effective detection methods. Previous deepfake audio generation methods typic…
Genuine-Focused Learning using Mask AutoEncoder for Generalized Fake Audio Detection
Xiaopeng Wang, Ruibo Fu, Zhengqi Wen +9
The generalization of Fake Audio Detection (FAD) is critical due to the emergence of new spoofing techniques. Traditional FAD methods often focus solely on distinguishing between g…